PlatYi-34B-Llama-Q-v3
![](/kyujinpy/PlatYi-34B-Llama-Q-v3/resolve/main/PlatYi.png)
Model Details
Model Developers Kyujin Han (kyujinpy)
Input Models input text only.
Output Models generate text only.
Model Architecture
PlatYi-34B-Llama-Q-v3 is an auto-regressive language model based on the Yi-34B transformer architecture.
Blog Link
Blog: [Coming soon...]
Github: [Coming soon...]
Base Model
chargoddard/Yi-34B-Llama
Training Dataset
garage-bAInd/Open-Platypus.
Fix some bugs
- Before model, there is some mistakes.
- I modified the templates and warmup_steps.
Notice
While training, I used Q-LoRA. The lora_r values is 64.
Model Benchmark
Open leaderboard
- Follow up as link.
Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
---|---|---|---|---|---|---|---|
PlatYi-34B-Llama-Q-v3 | 61.15 | 64.33 | 84.88 | 74.98 | 51.80 | 82.79 | 6.67 |
PlatYi-34B-Llama-Q-v2 | 67.88 | 61.09 | 85.09 | 76.59 | 52.65 | 82.79 | 49.05 |
PlatYi-34B-Llama-Q | 71.13 | 65.70 | 85.22 | 78.78 | 53.64 | 83.03 | 60.42 |
PlatYi-34B-Llama | 68.37 | 67.83 | 85.35 | 78.26 | 53.46 | 82.87 | 42.46 |
Yi-34B-Llama | 70.95 | 64.59 | 85.63 | 76.31 | 55.60 | 82.79 | 60.80 |
Yi-34B | 69.42 | 64.59 | 85.69 | 76.35 | 56.23 | 83.03 | 50.64 |
Implementation Code
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "kyujinpy/PlatYi-34B-Llama-Q-v3"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 61.15 |
AI2 Reasoning Challenge (25-Shot) | 64.33 |
HellaSwag (10-Shot) | 84.88 |
MMLU (5-Shot) | 74.98 |
TruthfulQA (0-shot) | 51.80 |
Winogrande (5-shot) | 84.21 |
GSM8k (5-shot) | 6.67 |
- Downloads last month
- 694
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for kyujinpy/PlatYi-34B-Llama-Q-v3
Dataset used to train kyujinpy/PlatYi-34B-Llama-Q-v3
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard64.330
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard84.880
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard74.980
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard51.800
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard84.210
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard6.670